Work

CLTC-PL: A Robust Mathematical Framework and Algorithm for InSAR Phase-Linking Using the Central Limit Theorem of Circular Statistics

Year: 2025

Type: article

Abstract: Phase-linking (PL) plays a crucial role in distributed scatterer (DS) InSAR, but conventional approaches often rely on strong prior assumptions about the underlying data distribution and involve solving highly nonlinear optimization problems. In this study, we propose a novel PL framework based on the central limit theorem for circular data (CLTC), which models interferometric phases through trigonometric moments and avoids any prior assumptions about the data distribution. The CLTC-PL formulation transforms the originally nonlinear PL problem into an inherently well-posed and locally linear weighted least-squares estimation, enabling efficient optimization with minimal iterations and error propagation analysis. The proposed method offers clear structural transparency and statistical interpretability, while having strong estimation performance. This work not only improves robust phase estimation, but also introduces a model-free framework where a multivariate normal distribution of trigonometric moments arises naturally via CLTC, allowing statistically grounded inference. Both simulated and real-data experiments validate the effectiveness of the proposed PL mathematical framework.

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2025.3591623

Authors: Shuyi Yao, Alejandro C. Frery, Timo Balz

Institutions: Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Victoria University of Wellington

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China